Land-Use/Land-Cover Change and Anthropogenic Causes Around Koupa Matapit Gallery Forest, West-Cameroon
Bibliographic record
Abstract
This study assesses land cover change of the Koupa Matapit forest gallery, West Cameroon, in relation to anthropogenic factors. Ethnobotanical surveys were conducted to investigate the relationships between the local population and the gallery forest; the spatio-temporal dynamics of the landscapes around the gallery forest were studied from the diachronic analysis of three Landsat TM satellite images of 1984, Landsat ETM + 1999 and Landsat OLI_TIRS of 2016, supplemented by verification missions on field. The satellite images were processed using ArcGIS and Erdas Imagine software. According to surveys, it should be noted that agriculture and livestock are the main economic activities of the population of Koupa Matapit, agriculture and fuel wood collection for energy were the main anthropogenic activities responsible for deforestation and degradation of the forest gallery. The collection of non-timber forest products (NTFPs) would have a significant implication in land use and cover changes. The results indicate that the extension of savannah/agricultural land (from 6989 ha in 1984 to 7604 ha in 2016) and bare soil/built up area (from 71 ha in 1984 to 342 ha in 2016) would have led to the disappearance of much of the forest area (1465 ha in 1984 to 580 ha in 2016). The rapid population growth of Koupa Matapit would be responsible for these pressures. There is an urgent need to implement appropriate land use policy in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".